Media content (e.g. images and videos) often exist in compressed form to reduce storage space and to facilitate transport. For example, a media server typically accesses compressed media and streams the compressed media to a client capable of decompressing the media for presentation. Compression is extensively used in transmission, storage and playback in various applications.
The compressed media are usually generated by the following process. First, raw media contents are predicted from their temporal and/or spatial neighbors. Second, the predicted residues are transformed to frequency domain. At last, the coefficients are quantized and entropy coded to generate the compressed representation. In general, natural images and videos contain rich edges and contours, which still exist after prediction. These edges constitute the high-frequency part of media, which are difficult to encode because the energy of signal becomes somewhat scattered after transformation to the frequency domain. Often edges and contours contain important structural media content, however, transform-based representation has a problem to preserve and utilize edges and contours.
For example, consider “mosquito noise”, which is a type of edge busyness distortion that appears near crisp edges of objects in MPEG and other video frames compressed using lossy techniques that rely on the discrete cosine transform (DCT). More specifically, mosquito noise occurs at decompression as the decoding engine approximates discarded data by inverting the transform model. In video, mosquito noise appears as frame-to-frame random aliasing at the edges (e.g., resembling a mosquito flying around a person's head where edges exist between the person's head and a solid background). In general, as TV and computer screens get larger, mosquito noise and other artifacts become more noticeable.
For image compression techniques that rely solely on the DCT (a Fourier-related transform similar to the discrete Fourier transform, but using only real numbers), edges and contours are totally invisible. Another type of transform, the wavelet transforms, is a time-frequency transform, however, wavelet based compression techniques only use structure information in context models for arithmetic coding. Consequently, DCT and wavelet techniques fall short in their ability to represent media in a manner that preserves edge and contour information. Further, in both DCT based and wavelet based compression techniques, it is not easy to access structure information in a compressed stream or a compressed file. Techniques are presented herein that allow for preservation of edge and contour information as well as access to such information.